Mapping Global Conflict

We will explore the data set from the Upsalla Data Conflict Program.

To start, download and clean the data. Then some light analysis and plotting. Finally, we will make a prediction using the insights we’ve gained.

Download Data and Prep

This is the nitty gritty of getting the data and putting it into a usable form. An important step, but a tedious one to read through. Feel free to skip ahead.

# get data
prac <- fromJSON('https://ucdpapi.pcr.uu.se/api/gedevents/20.1?pagesize=1000&StartDate=2019-01-01&EndDate=2019-12-31')

# extract results list
result <- prac$Result

# Null to NA function to apply through
null_to_na <- function(x) {
    
    for(i in 1:length(x))
        if(is.null(x[[i]])){
            x[[i]] <- NA
        } else {
            next
        }
    return(x)
    
}

# Set nulls to NA
result <- lapply(result, null_to_na)

# Initialize data frame with first element of results
c_df<- data.frame(result[[1]])

# Add the rest w/loop
for (i in 2:length(result)){
    c_df <- rbind(c_df, data.frame(result[[i]]))
}

# get next URL
URL <- prac$NextPageUrl
url_list <- c(URL, rep(NA, 50))

# Get list of URL's
for( i  in 2:39){
    listing <- fromJSON(URL)
    if (listing$NextPageUrl != ""){
        url_list[i] <- listing$NextPageUrl
        Sys.sleep(.2)
        URL <- listing$NextPageUrl
    } else {
        break
    }
}

# Manually subset to remove NA's
url_list <- url_list[!is.na(url_list)]

# This function should take a vector of URL's that return JSON, and
# give back data frames of data
get_all_data <- function(x) {
    # get Data
    data <- fromJSON(x)
    
    # subset Data
    data <- data$Result
    
    # Turn Nulls to NA's
    data <- lapply(data, null_to_na)
    
    # Initialize data frame with first element of results
    c_df<- data.frame(data[[1]])
    
    # Add the rest w/loop
    for (i in 2:length(data)){
        c_df <- rbind(c_df, data.frame(data[[i]]))
    }
    
    return(c_df)
}

# lappy over our URL with get_all_data
yes <- lapply(url_list, get_all_data)

# Collapse list of df's to single df
yes <- bind_rows(yes)

# Add page 1
c_df <- rbind(c_df, yes)

# As tibble
c_df <- c_df %>% 
    as_tibble(c_df)

# Create a civilian deaths categorical variable
c_df <- c_df %>% 
    mutate(civ_cat = case_when(deaths_civilians > 0 ~ 'yes', TRUE ~ 'no'))

# Save file for later use
#save(c_df, "data/conflict_19.Rds")
#load(file = "data/conflict_19.Rds")

Explore

Let’s look at some stuff.

Tables

Random Subset

set.seed(42)
kable(c_df %>%
        filter(best > 10) %>% 
        sample_n(5) %>%
        select(date_start, country, side_a, side_b, deaths_a, deaths_b, civ_cat) %>% 
        arrange(date_start), col.names = c('Date', 'Conflict Location', 'Side A', 'Side B', 'Deaths A', 'Deaths B', 'Civilian Causualties'), align = 'c', caption = 'Random Subset')

Random Subset
Date	Conflict Location	Side A	Side B	Deaths A	Deaths B	Civilian Causualties
2019-03-12T00:00:00	Nigeria	Government of Nigeria	IS	0	22	no
2019-05-14T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	17	no
2019-09-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	0	no
2019-09-19T00:00:00	DR Congo (Zaire)	Government of DR Congo (Zaire)	CMC	23	0	no
2019-10-03T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	24	no

Most Events By Country

kable(c_df %>%
        add_count(country) %>% 
        group_by(country) %>% 
        summarise(country = country, total_estimated = sum(best), n = n) %>%
        distinct() %>% 
        select(country, n, total_estimated) %>% 
        arrange(desc(n)) %>% 
        head(10), col.names = c('Conflict Location', 'Total Events', 'Total Deaths'), align = 'c', caption = 'Most Deaths')

Most Deaths
Conflict Location	Total Events	Total Deaths
Afghanistan	4682	30434
Syria	2242	10931
Mexico	786	11789
Nigeria	509	2437
Somalia	426	2221
DR Congo (Zaire)	416	2393
India	352	728
Brazil	263	1296
Iraq	246	803
Cameroon	239	858

Afghanistan had far and away the most conflicts, followed by Syria and Mexico.

Events with the Largest Death Counts

kable(c_df %>% 
        select(date_start, country, side_a, side_b, deaths_a, deaths_b, deaths_unknown, best, civ_cat) %>%
        arrange(desc(best)) %>%
        head(10), 
        col.names = c('Date', 'Conflict Location', 'Side A', 'Side B', 'Deaths A', 'Deaths B', 'Unknown Deaths', 'Best Estimate for Total Deaths', 'Civilian Causualties'), 
        align = 'c', 
        caption = 'Largest Death Count per Conflict Event in 2019')

Largest Death Count per Conflict Event in 2019
Date	Conflict Location	Side A	Side B	Deaths A	Unknown Deaths	Best Estimate for Total Deaths	Civilian Causualties
2019-01-01T00:00:00	Brazil	Comando Vermelho	GDE	0	739	739	no
2019-10-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	278	278	no
2019-12-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	272	272	no
2019-12-01T00:00:00	Mexico	Jalisco Cartel New Generation	La Familia	0	266	266	no
2019-09-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	250	250	no
2019-11-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	245	245	no
2019-10-01T00:00:00	Mexico	Jalisco Cartel New Generation	Sinaloa Cartel	0	231	231	no
2019-03-19T00:00:00	Syria	IS	SDF	20	165	230	yes
2019-07-01T00:00:00	Mexico	Jalisco Cartel New Generation	La Familia	0	229	229	no
2019-02-01T00:00:00	Mexico	Jalisco Cartel New Generation	Santa Rosa de Lima Cartel	0	226	226	no

We notice a few things from the table above. Mexico had many deadly conflict events in 2019, their dates are truncated to the first of the month, and nearly all deaths are classified as ‘unknown’. This leads me to believe that the deaths are not one single conflict event, but instead a collection of smaller events that are aggregated and reported at the end of the month.

USA Involved

# USA involved
kable(c_df %>%
    filter(side_b_new_id == 769 | side_a_new_id == 769) %>% 
        select(date_start, country, side_a, side_b, deaths_a, deaths_b, civ_cat) %>% 
        arrange(date_start), col.names = c('Date', 'Conflict Location', 'Side A', 'Side B', 'Deaths A', 'Deaths B', 'Civilian Causualties'), align = 'c', caption = 'USA Directly Involved')

USA Directly Involved
Date	Conflict Location	Side A	Side B	Deaths B	Civilian Causualties
2019-04-07T00:00:00	Afghanistan	Government of United States of America	al-Qaida	2	no
2019-05-21T00:00:00	Afghanistan	Government of United States of America	al-Qaida	2	no
2019-06-29T00:00:00	Afghanistan	Government of United States of America	al-Qaida	2	no
2019-06-29T00:00:00	Afghanistan	Government of United States of America	al-Qaida	1	no
2019-07-30T00:00:00	Afghanistan	Government of United States of America	al-Qaida	9	no
2019-07-30T00:00:00	Afghanistan	Government of United States of America	al-Qaida	4	no
2019-07-30T00:00:00	Afghanistan	Government of United States of America	al-Qaida	8	no
2019-09-07T00:00:00	Afghanistan	Government of United States of America	al-Qaida	9	no

Doesn’t appear to be a ton of direct US involvement in the 19 year old conflict with the Taliban.

# USA involved
kable(c_df %>%
    filter(side_a_new_id == 130 | side_a_new_id == 130) %>% 
        select(date_start, country, side_a, side_b, deaths_a, deaths_b, civ_cat) %>% 
        arrange(desc(deaths_b)) %>% 
        head(10), col.names = c('Date', 'Conflict Location', 'Side A', 'Side B', 'Deaths A', 'Deaths B', 'Civilian Causualties'), align = 'c', caption = 'Afghanistan Military')

Afghanistan Military
Date	Conflict Location	Side A	Side B	Deaths A	Deaths B	Civilian Causualties
2019-08-31T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	100	no
2019-04-06T00:00:00	Afghanistan	Government of Afghanistan	Taleban	24	99	no
2019-03-22T00:00:00	Afghanistan	Government of Afghanistan	Taleban	12	94	yes
2019-09-04T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	93	no
2019-03-26T00:00:00	Afghanistan	Government of Afghanistan	IS	0	87	no
2019-09-06T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	85	no
2019-09-14T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	84	no
2019-09-28T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	61	no
2019-11-24T00:00:00	Afghanistan	Government of Afghanistan	Taleban	8	60	no
2019-06-19T00:00:00	Afghanistan	Government of Afghanistan	Taleban	0	59	no

So it appears the US itself isn’t as active as the Afghanistan state military, however considering the US spends 38 billion (with a b) dollars in 2019 alone, well…

Civilian Deaths Globally

# How many?
kable(c_df %>% 
    count(civ_cat) %>% 
    arrange(n), caption = 'Civilian Deaths', col.names = c('Civilian Deaths', 'Number of Events'))

Civilian Deaths
Civilian Deaths	Number of Events
yes	2795
no	9689

Including Plots

Average Deaths Per Conflict

# Okay, loving this plot, group by country and rounded dates, sum deaths, plot
# Filtering by more than 300 events here.
c_df %>% 
    add_count(country) %>% 
    filter(n > 300) %>% 
    mutate(rounded_date = floor_date(as.Date(date_start), unit = 'month')) %>% 
    group_by(country, rounded_date) %>% 
    mutate(sum_deaths = sum(best)) %>% 
    select(country, sum_deaths, rounded_date) %>% 
    ggplot(aes(rounded_date, sum_deaths, color = country)) +
    geom_line() +
    theme(axis.text.x = element_text(angle = 45, vjust = .5), legend.position = 'none') +
    labs(x = 'Dates', y = 'Total Deaths', title = 'Deaths Per Month') +
    facet_wrap(~country, scales = 'free_y')

# Okay, really cool plot. Doesn't show st dev, but shows average deaths, plus sample
# size, ordered by most
c_df %>% 
    select(country, best, date_start, region, best) %>% 
    add_count(country) %>% 
    filter(n > 100) %>% 
    group_by(country) %>% 
    mutate(average = (sum(best)/n), sum_deaths = sum(best)) %>% 
    select(-best, -date_start) %>% 
    # Seems like a cheater way to limit number of rows
    summarise(region = unique(region),
          n = max(n), 
          average = max(average),
          sum_deaths = max(sum_deaths)) %>% 
    mutate(country = fct_reorder(country, -average)) %>% 
    ggplot(aes(country, average, fill = country))+
    geom_col() +
    geom_text(aes(label = paste0('n= ', n), angle = 90), hjust = 'top') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position =  'none') +
    labs(x = 'Country', y = 'Average Deaths per Violent Encounter 2019') +
    scale_y_continuous(n.breaks = 8)

Like we mention above, Mexico’s death counts per event are suspicious. There might be some aggregation going on.

Deaths per Conflict

# This is beautiful. Easy ggplot for histogram of deaths, w/ Region Facet
c_df %>%
    filter(best < 50) %>%
    ggplot(aes(x = best)) +
    geom_histogram(bins = 50, aes(fill=region)) + 
    facet_wrap(~region) +
    xlab('Deaths Best Estimate') +
    labs(title = 'Are some regions events more deadly?', y ='Number of Events', x='Number of Deaths')

They appear to be the same.

# OMG THIS IS IT. THE WORLD MAP! Thank you JULIA SILGE! 
world <- map_data('world')

# 
ggplot() +
    geom_map(data = world, map = world, aes(long, lat, map_id = region), 
             color = 'white', fill = 'gray50', alpha = .2) +
    geom_point(data = c_df, aes(longitude, latitude, color = as.factor(type_of_violence)), 
               alpha = .8) +
    theme(legend.title = element_text('Type of Violence')) +
    labs(title = 'Global Violence') +
    scale_color_brewer(palette = 'Set1', labels = c('State Based', 'Non-State', 'One-Sided')) +
    guides(color = guide_legend('Type of Violence'))

# Okay, kinda like, civilian deaths mapped globally
ggplot() +
    geom_map(data = world, map = world, aes(long, lat, map_id = region), 
             color = 'white', fill = 'gray50', alpha = .3) +
    geom_point(data = c_df, aes(longitude, latitude, 
                                color = as.factor(civ_cat), 
                                group = id), alpha = .8) +
    theme() +
    scale_color_brewer(palette = 'Dark2') +
    labs(title = 'Conflict Civilian Deaths 2019', x ='', y= '') +
    guides(color = guide_legend('Civilian Deaths'))

Lets look at January 2019 for a few select countries.

# Set up data frame
# January only, Afghanistan only, change to dates, select specific columns
c_jan_19_af <- c_df %>% 
    filter(country == 'Afghanistan') %>%
    select(id, best, latitude, longitude, side_a, side_b, date_start, date_end) %>% 
    mutate(date_start = as.Date(date_start), date_end = as.Date(date_end)) %>% 
    filter(date_start <= '2019-1-31' & date_start >= '2019-1-1') 

# Get shapefile -- https://hub.arcgis.com/datasets/2b63527870ef416bacf83bcaf388685f_0/data
afg_sf <- read_sf('afghanistan')


# Beautiful, needed ids and frame for plotly. Frame needs to be in numeric or prob character
# maybe as.Date as.char
afg <- 
    ggplot(data = c_jan_19_af) +
    geom_sf(data = afg_sf, fill = 'gray50', alpha =.1) +
    geom_point(data = c_jan_19_af, aes(longitude, latitude, ids = id,
                                       frame = as.character(date_start)),
               alpha = .8, color = 'darkred', size = 1) +
    labs(x = 'Longitude', 
         y = 'Latitude',
         title = 'Conflict in Afghanistan January 2019')

# Plotly instead?
afg <- ggplotly(afg, width = 500, height = 500)

# Need to re-run to see if this fixes sizing - 
afg %>% 
    animation_opts(1000, easing = "linear", redraw = FALSE)

# Set up data frame
# January only, India only, change to dates, select specific columns
c_jan_19_in <- c_df %>% 
    filter(country == 'India') %>%
    select(id, best, latitude, longitude, side_a, side_b, date_start, date_end) %>% 
    mutate(date_start = as.Date(date_start), date_end = as.Date(date_end)) %>% 
    filter(date_start <= '2019-1-31' & date_start >= '2019-1-1') 

# Get shapefile -- https://hub.arcgis.com/datasets/2b37b84e67374fb98577c20ef8be6c62_0
india_sf <- read_sf('india')

# Beautiful, needed ids and frame for plotly. Frame needs to be in numeric or prob character
# maybe as.Date as.char
ind <- 
    ggplot(data = c_jan_19_in) +
    geom_sf(data = india_sf, fill = 'gray50', alpha =.1) +
    geom_point(data = c_jan_19_in, aes(longitude, latitude, ids = id,
                                       frame = as.character(date_start)),
               alpha = .8, color = 'darkred', size = 1) +
    labs(x = 'Longitude', 
         y = 'Latitude',
         title = 'Conflict in India January 2019')

# Plotly instead?
ind <- ggplotly(ind, width = 500, height = 500)

# Add opts
ind %>% 
    animation_opts(1000, easing = "linear", redraw = FALSE)

Mapping Conflict

Carson Poe

12/28/2020

Mapping Global Conflict

Download Data and Prep

Explore

Tables

Including Plots